• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.3
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• pp.588-594
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• 2015

Although GPS technology for location positioning system has been widely used, it is difficult to be used in intelligent transport systems, due to the large positioning error and limited area for receiving radio signals. Thanks to the rapid development of LED technology, LED lights become popular in many applications. Especially, visible light communications (VLC) has raised a lot of interests because of the simultaneous functioning of LED illumination and communication. Recent studies on positioning system using VLC mainly focused on indoor environments and still difficult to satisfy positioning accuracy and simple implementation simultaneously. In this paper, we propose a positioning system based on VLC using the coordinate information of LEDs installed on the road infrastructure. Extracting the LED signal, obtained through VLC, from the easily accessible camera image, it is possible to estimate the position of the car on the road. Simulation results show that the proposed scheme can achieve a high positioning accuracy of 1 m when large number of pixels is utilized and the distance from the LED light is close.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.128-139
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• 2017

Railway is one of the public transportation systems along with shipping and aviation. With the recent introduction of high speed train, its proportion is increasing rapidly, which results in the higher risk of catastrophic failures. The wheel bearing to support the train is one of the important components requiring higher reliability and safety in this aspect. Recently, many studies have been made under the name of prognostics and health management (PHM), for the purpose of fault diagnosis and failure prognosis of the bearing under operation. Among them, the most important step is to extract a feature that represents the fault status properly and is useful for accurate remaining life prediction. However, the conventional features have shown some limitations that make them less useful since they fluctuate over time even after the signal de-noising or do not show a distinct pattern of degradation which lack the monotonic trend over the cycles. In this study, a new method for feature extraction is proposed based on the observation of relative frequency energy shifting over the cycles, which is then converted into the feature using the information entropy. In order to demonstrate the method, traditional and new features are generated and compared using the bearing data named FEMTO which was provided by the FEMTO-ST institute for IEEE 2012 PHM Data Challenge competition.